An Exploration of How Parents Did Waiting for Their Child’s Transplant: Inpatient, Outpatient, and Home Space-Times
Bibliographic record
Abstract
Current evidence shows that waiting for a child’s solid organ, stem cell, or bone marrow transplant can cause social, emotional, and psychological suffering for children and their families. Despite waiting being a central theme, little research has investigated what families do while they are waiting in hospital and home settings and what daily life looks like in these contexts. This narrative ethnographic study explored what waiting may look like for parents of children waiting to receive a solid organ, stem cell, or bone marrow transplant drawing on the notion of space-time. Six parents from four different families participated in interviews and observations that explored the questions: How do parents wait on a daily basis and what does waiting look like in the hospital and home? Our narrative analysis suggested that the structure, rhythms, and flow were complex and diverse in the hospital and home space-time. Inpatient space-time could afford parents comfort by having expectations managed, while unpredictability of outpatient space-time caused immense stress. Waiting at home was a paradox in that it could be busy, monotonous, and isolating. Findings contribute to conceptual and practical work exploring how parents do waiting for their child’s transplant on a daily basis and how they can be supported when they enter into unfamiliar illness narratives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".